Generating representations of acoustic sequences

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representation of acoustic sequences. One of the methods includes: receiving an acoustic sequence, the acoustic sequence comprising a respective acoustic feature representation at each of a plurality of time steps; processing the acoustic feature representation at an initial time step using an acoustic modeling neural network; for each subsequent time step of the plurality of time steps: receiving an output generated by the acoustic modeling neural network for a preceding time step, generating a modified input from the output generated by the acoustic modeling neural network for the preceding time step and the acoustic representation for the time step, and processing the modified input using the acoustic modeling neural network to generate an output for the time step; and generating a phoneme representation for the utterance from the outputs for each of the time steps.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.61/917,089, filed on Dec. 17, 2013. The disclosure of the priorapplication is considered part of and is incorporated by reference inthe disclosure of this application.

BACKGROUND

This specification relates to generating phoneme representations ofacoustic sequences.

Acoustic modeling systems receive an acoustic sequence and generate aphoneme representation of the acoustic sequence. The acoustic sequencefor a given utterance includes, for each of a set of time steps, anacoustic feature representation that characterizes the utterance at thecorresponding time step. The phoneme representation is a sequence ofphonemes or phoneme subdivisions that the acoustic modeling system hasclassified as representing the received acoustic sequence. An acousticmodeling system can be used in, for example, a speech recognitionsystem, e.g., in conjunction with a pronunciation modeling system and alanguage modeling system.

SUMMARY

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof receiving an acoustic sequence, the acoustic sequence representing anutterance, and the acoustic sequence comprising a respective acousticfeature representation at each of a plurality of time steps; processingthe acoustic feature representation at an initial time step using anacoustic modeling neural network to generate an output for the initialtime step; for each subsequent time step of the plurality of time steps:receiving the acoustic representation for the time step, receiving anoutput generated by the acoustic modeling neural network for a precedingtime step, generating a modified input from the output generated by theacoustic modeling neural network for the preceding time step and theacoustic representation for the time step, and processing the modifiedinput using the acoustic modeling neural network to generate an outputfor the time step; and generating a phoneme representation for theutterance from the outputs for each of the time steps.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.For a system of one or more computers to be configured to performparticular operations or actions means that the system has installed onit software, firmware, hardware, or a combination of them that inoperation cause the system to perform the operations or actions. For oneor more computer programs to be configured to perform particularoperations or actions means that the one or more programs includeinstructions that, when executed by data processing apparatus, cause theapparatus to perform the operations or actions.

The foregoing and other embodiments can each optionally include one ormore of the following features, alone or in combination. The acousticmodeling neural network can be a feed-forward neural network. Theacoustic modeling neural network can be a recurrent neural network. Theacoustic modeling neural network can be a long short-term memory (LSTM)neural network. The output generated by the acoustic modeling neuralnetwork for each time step can be a set of scores for a set of phonemesor phoneme subdivisions, wherein the score for each phoneme or phonemesubdivision represents a likelihood that the phoneme or phonemesubdivision is a representation of the utterance at the time step.Generating the modified input can include appending the set of scoresfor the preceding time step to the acoustic feature representation forthe time step. Generating the modified input can include appending dataidentifying a highest-scoring phoneme or phoneme subdivision accordingto the set of scores for the preceding time step to the acoustic featurerepresentation for the time step. The set of scores can define aprobability distribution over a set of Hidden Markov Model (HMM) states.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. By combining the output from the preceding timestep with the input for the current time step to generate a modifiedinput for use by an acoustic modeling neural network in generating theoutput for the current time step, an acoustic modeling system can moreaccurately predict the phoneme representation for an input sequence.That is, because the acoustic modeling neural network is explicitlyprovided with the preceding output as part of the input for the currenttime step, the prediction made by the acoustic modeling neural networkfor the current time step can be more accurate.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example acoustic modeling system.

FIG. 2 is a flow diagram of an example process for generating a phonemerepresentation of an acoustic sequence.

FIG. 3 is a flow diagram of an example process for processing anacoustic feature representation.

FIG. 4 is diagram of exemplary computing devices.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 shows an example acoustic modeling system 100. The acousticmodeling system 100 is an example of a system implemented as computerprograms on one or more computers in one or more locations, in which thesystems, components, and techniques described below can be implemented.

The acoustic modeling system 100 receives acoustic sequences andgenerates phoneme representations of the received acoustic sequences.For example, the acoustic modeling system 100 can receive an acousticsequence 102 and generate a phoneme representation 114 for the acousticsequence 102.

The acoustic sequence 102 is a sequence that includes a respectiveacoustic feature representation, e.g., an acoustic feature vector, foreach of a set of time steps. Each acoustic feature representationcharacterizes an utterance at the corresponding time step. The phonemerepresentation 114 for the acoustic sequence 102 includes a respectiveset of scores for each of the time steps. The set of scores for a giventime step includes a respective score for each of a set of phonemes or aset of phoneme subdivisions. The score for a given phoneme or phonemesubdivision at a given time step represents a likelihood that thephoneme or phoneme subdivision is a representation of the utterance atthe time step. For example, the set of scores may be probabilities thatdefine a probability distribution over a set of Hidden Markov Model(HMM) states.

The acoustic modeling system 100 includes an input layer 106 and anacoustic modeling neural network 110. The acoustic modeling neuralnetwork 110 is a neural network that has been configured, e.g., throughtraining, to receive an input from the input layer 106 and generate anoutput from the received input. In particular, the acoustic modelingneural network 110 receives an input for a given time step in anacoustic sequence from the input layer 106 and processes the input togenerate a set of scores in accordance with current values of theparameters of the acoustic modeling neural network. As described above,the set of scores includes a score for each of a set of phonemes or aset of phoneme subdivisions, with the score for a given phoneme orphoneme subdivision representing a likelihood that the phoneme orphoneme subdivision represents the utterance at the time step.

The acoustic modeling neural network 110 can be a feed-forward neuralnetwork, e.g., a deep neural network that includes one or more layers ofnon-linear operations. An example deep neural network that can be usedfor generating phoneme representations from received inputs is describedin more detail in Deep Neural Networks for Acoustic Modeling in SpeechRecognition, G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly,A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, IEEESignal Processing Magazine, November 2012, page 82.

Alternatively, the acoustic modeling neural network 110 can be arecurrent neural network, e.g., a Long Short-Term Memory (LSTM) neuralnetwork. An LSTM neural network is a neural network that has one or moreLSTM memory blocks. An example LSTM neural network that can be used togenerate phoneme representations from received inputs is described inmore detail in Long Short-Term Memory Based Recurrent Neural NetworkArchitectures for Large Vocabulary Speech Recognition, H. Sak, A.Senior, F. Beaufays, available at http://arxiv.org/pdf/1402.1128.

The input layer 106 receives acoustic feature representations at eachtime step of an input acoustic sequence and, at each time step,generates an input to be provided to the acoustic modeling neuralnetwork 110 for processing. In particular, for each time step t in theacoustic sequence subsequent to the initial time step in the sequence,the input layer 106 receives the feature representation x_(t) at thetime step and the output s_(t-1) generated by the acoustic modelingneural network 110 for the preceding time step in the acoustic sequence.The input layer 106 generates a modified input x_(t), s_(t-1) for thecurrent time step from the feature representation x_(t) and thepreceding output s_(t-1). Generating a modified input is described inmore detail below with reference to FIG. 3. The input layer 106 thenprovides the modified input x_(t), s_(t-1) to the acoustic modelingneural network 110 for use in generating the output for the time step t.

Once the acoustic modeling neural network 110 has generated an outputfor each time step in the acoustic sequence, the acoustic modelingsystem 100 generates the phoneme representation for the acousticsequence from the generated outputs. In some implementations, thephoneme representation includes the set of scores for each time step. Insome other implementations, the system selects the phoneme or phonemesubdivisions having the highest score at each time step and a sequenceof the selected phonemes or phoneme subdivisions as the phonemerepresentation for the acoustic sequence.

FIG. 2 is a flow diagram of an example process 200 for generating aphoneme representation of an acoustic sequence. For convenience, theprocess 200 will be described as being performed by a system of one ormore computers located in one or more locations. For example, anacoustic modeling system, e.g., the acoustic modeling system 100 of FIG.1, appropriately programmed, can perform the process 200.

The system receives an acoustic sequence representing an utterance (step202). The acoustic sequence includes a respective acoustic featurerepresentation of the utterance at each of a set of time steps.

The system processes the acoustic feature representation at an initialtime step using an acoustic modeling neural network, e.g., the acousticmodeling neural network 110 of FIG. 1, to generate an output for theinitial time step (step 204). In some implementations, the acousticfeature representation at the initial time step is a pre-determinedinput that signifies that inputs that represent the utterance willfollow, e.g., a vector of pre-determined values, e.g., a vector ofzeroes.

The system processes the acoustic feature representation at eachsubsequent time step using the acoustic modeling neural network togenerate a respective output for each of the time steps (step 206).Generally, for each subsequent time step, the system provides as inputto the acoustic modeling neural network a modified input generated fromthe acoustic feature representation for the time step and the outputgenerated by the acoustic modeling neural network for the preceding timestep. Processing the acoustic feature representations at subsequent timesteps is described below with reference to FIG. 3.

The system generates a phoneme representation of the acoustic sequenceusing the outputs generated by the acoustic modeling neural network foreach time step (step 208). In some implementations, the phonemerepresentation includes the set of scores for each time step. In someother implementations, the system selects the phoneme or phonemesubdivision having the highest score at each time step and generates asequence of the selected phonemes or phoneme subdivisions as the phonemerepresentation for the acoustic sequence.

FIG. 3 is a flow diagram of an example process 300 for processing anacoustic feature representation. For convenience, the process 300 willbe described as being performed by a system of one or more computerslocated in one or more locations. For example, an acoustic modelingsystem, e.g., the acoustic modeling system 100 of FIG. 1, appropriatelyprogrammed, can perform the process 300.

The system receives an acoustic feature representation at a current timestep in an acoustic sequence (step 302).

The system receives an output generated by an acoustic modeling neuralnetwork for a preceding time step in the acoustic sequence (step 304).The output for the preceding time step is a set of scores, with eachscore corresponding to a respective phoneme or phoneme subdivision.

The system generates a modified input from the acoustic featurerepresentation and the output for the preceding time step (step 306). Insome implementations, the system generates the modified input byappending the set of scores to the acoustic feature representation. Forexample, if the acoustic feature representation is a vector of features,the system can generate a modified vector by appending a vector ofscores to the tail of the vector of features. In some implementations,the system generates the modified input by appending data identifyingone or more highest-scoring phonemes or phoneme subdivisions. Forexample, the system can append to the feature representation a vectorthat identifies the highest-scoring phonemes or phoneme subdivisions,e.g., a score vector that has a zero at each position other thanpositions corresponding to a threshold number of highest-scoringphonemes or phoneme subdivisions. The value of the vector at thosepositions can be a pre-determined value, e.g., one, or can be the scoreassigned to the corresponding phoneme or phoneme subdivision.

The system processes the modified input using the acoustic modelingneural network to generate an output for the current time step (step308). That is, the acoustic modeling neural network processes themodified input and generates the set of scores for the current time stepin accordance with current values of the parameters of the acousticmodeling neural network.

After trained values of the parameters of the acoustic modeling neuralnetwork have been determined, the processes 200 and 300 can be performedfor an acoustic sequence for which the desired phoneme representation isnot known, e.g., to generate a predicted phoneme representation for theacoustic sequence. The processes 200 and 300 can also be performed foreach time step of a training sequence, i.e., an acoustic sequence forwhich the desired phoneme representation is already known, as part of atraining process to determine the trained values of the parameters ofthe acoustic modeling neural network. The training process can be aconventional training process that is appropriate for training theacoustic modeling neural network. For example, if the acoustic modelingneural network is an LSTM neural network or other kind of recurrentneural network, the training process may be a backpropagation throughtime training process.

FIG. 4 shows an example of a computing device 400 and a mobile computingdevice 450 that can be used to implement the techniques described here.The computing device 400 is intended to represent various forms ofdigital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers. The mobile computing device 450 is intended torepresent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be examples only, andare not meant to be limiting.

The computing device 400 includes a processor 402, a memory 404, astorage device 406, a high-speed interface 408 connecting to the memory404 and multiple high-speed expansion ports 410, and a low-speedinterface 412 connecting to a low-speed expansion port 414 and thestorage device 406. Each of the processor 402, the memory 404, thestorage device 406, the high-speed interface 408, the high-speedexpansion ports 410, and the low-speed interface 412, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 402 can process instructionsfor execution within the computing device 400, including instructionsstored in the memory 404 or on the storage device 406 to displaygraphical information for a GUI on an external input/output device, suchas a display 416 coupled to the high-speed interface 408. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 404 stores information within the computing device 400. Insome implementations, the memory 404 is a volatile memory unit or units.In some implementations, the memory 404 is a non-volatile memory unit orunits. The memory 404 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 406 is capable of providing mass storage for thecomputing device 400. In some implementations, the storage device 406may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 402), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 404, the storage device 406, or memory on theprocessor 402).

The high-speed interface 408 manages bandwidth-intensive operations forthe computing device 400, while the low-speed interface 412 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 408 iscoupled to the memory 404, the display 416 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 410,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 412 is coupled to the storagedevice 406 and the low-speed expansion port 414. The low-speed expansionport 414, which may include various communication ports (e.g., USB,Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 400 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 420, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 422. It may also be implemented as part of a rack server system424. Alternatively, components from the computing device 400 may becombined with other components in a mobile device (not shown), such as amobile computing device 450. Each of such devices may contain one ormore of the computing device 400 and the mobile computing device 450,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 450 includes a processor 452, a memory 464,an input/output device such as a display 454, a communication interface466, and a transceiver 468, among other components. The mobile computingdevice 450 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 452, the memory 464, the display 454, the communicationinterface 466, and the transceiver 468, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 452 can execute instructions within the mobile computingdevice 450, including instructions stored in the memory 464. Theprocessor 452 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 452may provide, for example, for coordination of the other components ofthe mobile computing device 450, such as control of user interfaces,applications run by the mobile computing device 450, and wirelesscommunication by the mobile computing device 450.

The processor 452 may communicate with a user through a controlinterface 458 and a display interface 456 coupled to the display 454.The display 454 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface456 may comprise appropriate circuitry for driving the display 454 topresent graphical and other information to a user. The control interface458 may receive commands from a user and convert them for submission tothe processor 452. In addition, an external interface 462 may providecommunication with the processor 452, so as to enable near areacommunication of the mobile computing device 450 with other devices. Theexternal interface 462 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 464 stores information within the mobile computing device450. The memory 464 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 474 may also beprovided and connected to the mobile computing device 450 through anexpansion interface 472, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 474 mayprovide extra storage space for the mobile computing device 450, or mayalso store applications or other information for the mobile computingdevice 450. Specifically, the expansion memory 474 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 474 may be provide as a security module for the mobilecomputing device 450, and may be programmed with instructions thatpermit secure use of the mobile computing device 450. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier. thatthe instructions, when executed by one or more processing devices (forexample, processor 452), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices, such as one or more computer- or machine-readablemediums (for example, the memory 464, the expansion memory 474, ormemory on the processor 452). In some implementations, the instructionscan be received in a propagated signal, for example, over thetransceiver 468 or the external interface 462.

The mobile computing device 450 may communicate wirelessly through thecommunication interface 466, which may include digital signal processingcircuitry where necessary. The communication interface 466 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 468 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth, WiFi, or other such transceiver (not shown). In addition, aGPS (Global Positioning System) receiver module 470 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 450, which may be used as appropriate by applicationsrunning on the mobile computing device 450.

The mobile computing device 450 may also communicate audibly using anaudio codec 460, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 460 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 450. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 450.

The mobile computing device 450 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 480. It may also be implemented aspart of a smart-phone 482, personal digital assistant, or other similarmobile device.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non transitory program carrier for execution by, or to controlthe operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on an artificiallygenerated propagated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal, that is generated to encodeinformation for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit), or a GPGPU (General purpose graphics processingunit). The apparatus can also include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program (which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code) can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, e.g., one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files,e.g., files that store one or more modules, sub programs, or portions ofcode. A computer program can be deployed to be executed on one computeror on multiple computers that are located at one site or distributedacross multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Computers suitable for the execution of a computer program include, byway of example, can be based on general or special purposemicroprocessors or both, or any other kind of central processing unit.Generally, a central processing unit will receive instructions and datafrom a read only memory or a random access memory or both. The essentialelements of a computer are a central processing unit for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments can also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various system modulesand components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A method comprising: receiving an acousticsequence, the acoustic sequence representing an utterance, and theacoustic sequence comprising a respective acoustic featurerepresentation at each of a plurality of time steps; processing theacoustic feature representation at an initial time step using anacoustic modeling neural network to generate an output for the initialtime step; for each subsequent time step of the plurality of time steps:receiving the acoustic representation for the time step, receiving anoutput generated by the acoustic modeling neural network for a precedingtime step, generating a modified input from the output generated by theacoustic modeling neural network for the preceding time step and theacoustic representation for the time step, and processing the modifiedinput using the acoustic modeling neural network to generate an outputfor the time step; and generating a phoneme representation for theutterance from the outputs for each of the time steps.
 2. The method ofclaim 1, wherein the acoustic modeling neural network is a feed-forwardneural network.
 3. The method of claim 1, wherein the acoustic modelingneural network is a recurrent neural network.
 4. The method of claim 3,wherein the acoustic modeling neural network is a long short-term memory(LSTM) neural network.
 5. The method of claim 1, wherein the outputgenerated by the acoustic modeling neural network for each time step isa set of scores for a set of phonemes or phoneme subdivisions, whereinthe score for each phoneme or phoneme subdivision represents alikelihood that the phoneme or phoneme subdivision is a representationof the utterance at the time step.
 6. The method of claim 5, whereingenerating the modified input comprises appending the set of scores forthe preceding time step to the acoustic feature representation for thetime step.
 7. The method of claim 5, wherein generating the modifiedinput comprises appending data identifying a highest-scoring phoneme orphoneme subdivision according to the set of scores for the precedingtime step to the acoustic feature representation for the time step. 8.The method of claim 5, wherein the set of scores defines a probabilitydistribution over a set of Hidden Markov Model (HMM) states.
 9. A systemcomprising one or more computers and one or more storage devices storinginstructions that when executed by the one or more computers causes theone or more computers to perform operations comprising: receiving anacoustic sequence, the acoustic sequence representing an utterance, andthe acoustic sequence comprising a respective acoustic featurerepresentation at each of a plurality of time steps; processing theacoustic feature representation at an initial time step using anacoustic modeling neural network to generate an output for the initialtime step; for each subsequent time step of the plurality of time steps:receiving the acoustic representation for the time step, receiving anoutput generated by the acoustic modeling neural network for a precedingtime step, generating a modified input from the output generated by theacoustic modeling neural network for the preceding time step and theacoustic representation for the time step, and processing the modifiedinput using the acoustic modeling neural network to generate an outputfor the time step; and generating a phoneme representation for theutterance from the outputs for each of the time steps.
 10. The system ofclaim 9, wherein the acoustic modeling neural network is a feed-forwardneural network.
 11. The system of claim 9, wherein the acoustic modelingneural network is a recurrent neural network.
 12. The system of claim11, wherein the acoustic modeling neural network is a long short-termmemory (LSTM) neural network.
 13. The system of claim 9, wherein theoutput generated by the acoustic modeling neural network for each timestep is a set of scores for a set of phonemes or phoneme subdivisions,wherein the score for each phoneme or phoneme subdivision represents alikelihood that the phoneme or phoneme subdivision is a representationof the utterance at the time step.
 14. The system of claim 13, whereingenerating the modified input comprises appending the set of scores forthe preceding time step to the acoustic feature representation for thetime step.
 15. The system of claim 13, wherein generating the modifiedinput comprises appending data identifying a highest-scoring phoneme orphoneme subdivision according to the set of scores for the precedingtime step to the acoustic feature representation for the time step. 16.The system of claim 13, wherein the set of scores defines a probabilitydistribution over a set of Hidden Markov Model (HMM) states.
 17. Acomputer storage medium encoded with a computer program, the computerprogram comprising instructions that when executed by the one or morecomputers cause the one or more computers to perform operationscomprising: receiving an acoustic sequence, the acoustic sequencerepresenting an utterance, and the acoustic sequence comprising arespective acoustic feature representation at each of a plurality oftime steps; processing the acoustic feature representation at an initialtime step using an acoustic modeling neural network to generate anoutput for the initial time step; for each subsequent time step of theplurality of time steps: receiving the acoustic representation for thetime step, receiving an output generated by the acoustic modeling neuralnetwork for a preceding time step, generating a modified input from theoutput generated by the acoustic modeling neural network for thepreceding time step and the acoustic representation for the time step,and processing the modified input using the acoustic modeling neuralnetwork to generate an output for the time step; and generating aphoneme representation for the utterance from the outputs for each ofthe time steps.
 18. The computer storage medium of claim 17, wherein theoutput generated by the acoustic modeling neural network for each timestep is a set of scores for a set of phonemes or phoneme subdivisions,wherein the score for each phoneme or phoneme subdivision represents alikelihood that the phoneme or phoneme subdivision is a representationof the utterance at the time step.
 19. The computer storage medium ofclaim 18, wherein generating the modified input comprises appending theset of scores for the preceding time step to the acoustic featurerepresentation for the time step.
 20. The computer storage medium ofclaim 18, wherein generating the modified input comprises appending dataidentifying a highest-scoring phoneme or phoneme subdivision accordingto the set of scores for the preceding time step to the acoustic featurerepresentation for the time step.